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Co-designing AI Education Curriculum with Cross-Disciplinary High School Teachers
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Qualitative study of five co-design sessions with eight cross-disciplinary high school teachers on how they consider teaching about AI in art, math, and social studies and how they view AI tools in instruction.
Problem
High school teachers across disciplines want to teach about and with AI, but AI curricula have been mostly CS-focused and seldom co-designed with teachers from varied subjects; teachers have limited time and resources to build AI lessons.
Approach
Uses a dual-purpose co-design process in which the sessions themselves are data, examining how teachers from STEM and humanities disciplines frame AI (technical vs. ethical), identify "dual exploration" between AI and disciplinary frameworks, and weigh the benefits and distractions of imperfect AI tools.
Key results
- Eight teachers across five co-design sessions (Nov 2022 - Mar 2023) produced themes including a perceived dichotomy between technical skills and ethics in AI teaching.
- Teachers identified 'dual exploration' opportunities, e.g., applying history's OPCVL and ELA's SOAPSTone frameworks to AI-generated art.
- Teachers framed limitations of AI tools (e.g., DALL·E anatomical errors) both as discussion/reflection opportunities and as potential distractions.
Surprising findings
- Experienced CS and engineering teachers still identified crucial gaps in their technical AI knowledge, and co-design contribution did not equal readiness to teach about AI.
Limitations
- Small qualitative sample of eight teachers, not generalizable.
- Participants had prior relationships with researchers, possible social desirability bias.
- Conducted in a W.E.I.R.D. societal context.
Experimental setup
Datasets: co-design session recordings and transcripts (about 13 hours)
Metrics: thematic analysis themes
Builds on: Research-Practice Partnerships design justice AI4K12 big ideas
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